Recent developments in the PySCF program package

Abstract

PYSCF is a Python-based general-purpose electronic structure platform that both supports first-principles simulations of molecules and solids, as well as accelerates the development of new methodology and complex computational workflows. The present paper explains the design and philosophy behind PYSCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PYSCF as a development environment. We then summarize the capabilities of PYSCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PYSCF across the domains of quantum chemistry, materials science, machine learning and quantum information science.

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• QMCkl: A Kernel Library for Quantum Monte Carlo Applications
• LibppRPA: An Open-Source Library for Particle-Particle Random Phase Approximation
• Highly accurate ab-initio thermochemistry via real-space quantum Monte Carlo: Benzene
• Resource Estimation for VQE on Small Molecules: Impact of Fermion Mappings and Hamiltonian Reductions
• Quantitative Description of Strongly Correlated Materials by Combining Downfolding Techniques and Tensor Networks
• A quantum eigenvalue solver based on tensor networks
• Scalable and predictive spectra of correlated molecules with moment truncated iterated perturbation theory
• Enhanced Krylov Methods for Molecular Hamiltonians: Reduced Memory Cost and Complexity Scaling via Tensor Hypercontraction
• Influence of Bi Alloying on GaAs Valence Band Structure
• Encoding electronic ground-state information with variational even-tempered basis sets
• Energy-filtered excited states and real-time dynamics served in a contour integral
• Numerical investigation of the quantum inverse algorithm on small molecules
• Efficient Random Phase Approximation for Diradicals
• Machine Learning Green's Functions of Strongly Correlated Hubbard Models
• Fast Evaluation of Unbiased Atomic Forces in ab initio Variational Monte Carlo via the Lagrangian Technique
• Coupled electron pair-type approximations for tensor product state wavefunctions
• Benchmarking Quantum Simulation of Chemical Hamiltonians using the Sorted-List Encoding
• Reducing the Cost of Energy Differences in Variational Monte Carlo with Spotlight Sampling
• A charge-density machine-learning workflow for computing the infrared spectrum of molecules
• Bias-preserving computation with the bit-flip code
• Resolving Finite-Size Errors in EOM-CCSD Band Gaps of Solids with Interacting-Bath Dynamical Embedding Theory
• Approaching the Basis Set Limit in Gaussian-Orbital-Based Periodic Calculations with Transferability: Performance of Pure Density Functionals for Simple Semiconductors
• Precise Quantum Chemistry calculations with few Slater Determinants
• Controlling $⟨{\hat{S}}^2⟩$ in Broken-symmetry Density Functional Theory Calculations via Constrained Optimization